Keras CAE 检查目标时出错：预期 conv2d_7 的形状为 (252, 252, 3)，但得到的数组形状为 (256, 256, 3)

Question

我设置了卷积层和池化层，然后去卷积和去池，输入的形状是256*256*3的图像，但是最后有一个形状错误：

def build_auto_encode_model(shape=(256,256,3)):

    input_img = Input(shape=shape)  

    x = Convolution2D(16, (3, 3), activation='relu', padding='same')(input_img)  
    x = MaxPooling2D((2, 2), padding='same')(x)  
    x = Convolution2D(8, (3, 3), activation='relu', padding='same')(x)  
    x = MaxPooling2D((2, 2), padding='same')(x)  
    x = Convolution2D(8, (3, 3), activation='relu', padding='same')(x)  
    encoded = MaxPooling2D((2, 2), padding='same')(x)  

    x = Convolution2D(8, (3, 3), activation='relu', padding='same')(encoded)  
    x = UpSampling2D((2, 2))(x)  
    x = Convolution2D(8, (3, 3), activation='relu', padding='same')(x)  
    x = UpSampling2D((2, 2))(x)  
    x = Convolution2D(16, (3, 3), activation='relu')(x)  
    x = UpSampling2D((2, 2))(x)  
    decoded = Convolution2D(3, (3, 3), activation='sigmoid', padding='same')(x)  

    encoder = Model(inputs=input_img, outputs=encoded)

    autoencoder = Model(inputs=input_img, outputs=decoded)  
    autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy') 
    return encoder, autoencoder

def train_auto_encode_model(encoder_model_path="./data/encoder.h5"):
    X = np.load("data/train.npy")
    X_train = X[int(round(X.shape[0] * 0.2)):, :]
    X_test = X[0:int(round(X.shape[0] * 0.2)), :]

    encoder, autoencoder = build_auto_encode_model()
    autoencoder.fit(X_train, X_train, epochs=10, batch_size=64, shuffle=True, validation_data=(X_test, X_test))
    encoder.save(encoder_model_path)

下面是我得到的错误：

Error when checking target: expected conv2d_7 to have shape (252, 252, 3) but got array with shape (256, 256, 3)

错误回溯：

Answer 1

‌通过使用autoencoder.summary()，您将看到最后一个Conv2D层的输出形状是(None, 252, 252, 3)；因此形状(256,256,3)的标签不兼容。这个问题的原因是您忘记设置上一个Conv2D层的padding参数。将其设置为'same'可以解决此问题：

x = Convolution2D(16, (3, 3), activation='relu', padding='same')(x)